install.packages("here")Lesson 2: Working with tabular data
1 Prepare the R environment for this lesson
For this lesson we’ll need four packages:
- palmerpenguins
- here
- readr
- dplyr
First, we need to install the here package, using the install.packages() function.
Note, the readr and dplyr packages are part of the tidyverse, so we don’t need to install them separately. Now we use the library() function to load all of these packages.
library(here)here() starts at C:/Users/nickopotamus/Projects/ITMAT_office_houRslibrary(palmerpenguins)
library(readr)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionThe output from this code indicates R successfully loaded these packages. The message we get from the here package tells us which directory on our computers it’s using as the “root” directory (more on what this means below). The other warning messages explains that dplyr includes several functions (e.g. filter() and lag()) that have the same names as functions from the stats and base packages. R’s default behavior for resolving these naming conflicts is to use the version of the function loaded most recently into memory. The warning lets us know that if we run any of these functions, R will use the version of these functions from the dplyr package and not the stats/base package.
The potential for naming conflicts like this is another reason why we only load the packages we need for the current analyses.
RStudio’s Tab-Complete List Includes Package Names for Each Function
When we start to type a function’s name, and RStudio’s tab-completion prompt opens up, the package for each function is listed in curly bracers to the right of the function’s name. As we’re writing code, we can use this make sure we’re using the correct version of the function.
2 Reading data from files
We have a file named penguins.csv in the DATA/ directory. This data file is in .csv (comma-separated values) format and contains the same information as the penguins data frame we used in the previous lesson. We can view the raw contents of this file by using the Files tab in the lower right pane of the RStudio window. Navigate to the DATA/ directory, click on the penguins.csv file, then select the “View File” option.
2.1 Base R
We can read the data from this file using the base R function read.csv() and store the data in a variable named penguin_data_from_base_r.
penguin_data_from_base_r <- read.csv(file = here::here("DATA/penguins.csv"))In R, <- is called the assignment operator. The assignment operator takes the output from the function to its right (read.csv()) and assigns it to the variable to its right (penguin_data_from_base_r). Put differently, we’re storing the contents of the ‘penguins.csv’ file in ‘penguin_data_from_base_r’.
We can look at the contents of penguin_data_from_base_r by entering the variable name into the R console.
penguin_data_from_base_r species island bill_length_mm bill_depth_mm flipper_length_mm
1 Adelie Torgersen 39.1 18.7 181
2 Adelie Torgersen 39.5 17.4 186
3 Adelie Torgersen 40.3 18.0 195
4 Adelie Torgersen NA NA NA
5 Adelie Torgersen 36.7 19.3 193
6 Adelie Torgersen 39.3 20.6 190
7 Adelie Torgersen 38.9 17.8 181
8 Adelie Torgersen 39.2 19.6 195
9 Adelie Torgersen 34.1 18.1 193
10 Adelie Torgersen 42.0 20.2 190
11 Adelie Torgersen 37.8 17.1 186
12 Adelie Torgersen 37.8 17.3 180
13 Adelie Torgersen 41.1 17.6 182
14 Adelie Torgersen 38.6 21.2 191
15 Adelie Torgersen 34.6 21.1 198
16 Adelie Torgersen 36.6 17.8 185
17 Adelie Torgersen 38.7 19.0 195
18 Adelie Torgersen 42.5 20.7 197
19 Adelie Torgersen 34.4 18.4 184
20 Adelie Torgersen 46.0 21.5 194
21 Adelie Biscoe 37.8 18.3 174
22 Adelie Biscoe 37.7 18.7 180
23 Adelie Biscoe 35.9 19.2 189
24 Adelie Biscoe 38.2 18.1 185
25 Adelie Biscoe 38.8 17.2 180
26 Adelie Biscoe 35.3 18.9 187
27 Adelie Biscoe 40.6 18.6 183
28 Adelie Biscoe 40.5 17.9 187
29 Adelie Biscoe 37.9 18.6 172
30 Adelie Biscoe 40.5 18.9 180
31 Adelie Dream 39.5 16.7 178
32 Adelie Dream 37.2 18.1 178
33 Adelie Dream 39.5 17.8 188
34 Adelie Dream 40.9 18.9 184
35 Adelie Dream 36.4 17.0 195
36 Adelie Dream 39.2 21.1 196
37 Adelie Dream 38.8 20.0 190
38 Adelie Dream 42.2 18.5 180
39 Adelie Dream 37.6 19.3 181
40 Adelie Dream 39.8 19.1 184
41 Adelie Dream 36.5 18.0 182
42 Adelie Dream 40.8 18.4 195
43 Adelie Dream 36.0 18.5 186
44 Adelie Dream 44.1 19.7 196
45 Adelie Dream 37.0 16.9 185
46 Adelie Dream 39.6 18.8 190
47 Adelie Dream 41.1 19.0 182
48 Adelie Dream 37.5 18.9 179
49 Adelie Dream 36.0 17.9 190
50 Adelie Dream 42.3 21.2 191
51 Adelie Biscoe 39.6 17.7 186
52 Adelie Biscoe 40.1 18.9 188
53 Adelie Biscoe 35.0 17.9 190
54 Adelie Biscoe 42.0 19.5 200
55 Adelie Biscoe 34.5 18.1 187
56 Adelie Biscoe 41.4 18.6 191
57 Adelie Biscoe 39.0 17.5 186
58 Adelie Biscoe 40.6 18.8 193
59 Adelie Biscoe 36.5 16.6 181
60 Adelie Biscoe 37.6 19.1 194
61 Adelie Biscoe 35.7 16.9 185
62 Adelie Biscoe 41.3 21.1 195
63 Adelie Biscoe 37.6 17.0 185
64 Adelie Biscoe 41.1 18.2 192
65 Adelie Biscoe 36.4 17.1 184
66 Adelie Biscoe 41.6 18.0 192
67 Adelie Biscoe 35.5 16.2 195
68 Adelie Biscoe 41.1 19.1 188
69 Adelie Torgersen 35.9 16.6 190
70 Adelie Torgersen 41.8 19.4 198
71 Adelie Torgersen 33.5 19.0 190
72 Adelie Torgersen 39.7 18.4 190
73 Adelie Torgersen 39.6 17.2 196
74 Adelie Torgersen 45.8 18.9 197
75 Adelie Torgersen 35.5 17.5 190
76 Adelie Torgersen 42.8 18.5 195
77 Adelie Torgersen 40.9 16.8 191
78 Adelie Torgersen 37.2 19.4 184
79 Adelie Torgersen 36.2 16.1 187
80 Adelie Torgersen 42.1 19.1 195
81 Adelie Torgersen 34.6 17.2 189
82 Adelie Torgersen 42.9 17.6 196
83 Adelie Torgersen 36.7 18.8 187
84 Adelie Torgersen 35.1 19.4 193
85 Adelie Dream 37.3 17.8 191
86 Adelie Dream 41.3 20.3 194
87 Adelie Dream 36.3 19.5 190
88 Adelie Dream 36.9 18.6 189
89 Adelie Dream 38.3 19.2 189
90 Adelie Dream 38.9 18.8 190
91 Adelie Dream 35.7 18.0 202
92 Adelie Dream 41.1 18.1 205
93 Adelie Dream 34.0 17.1 185
94 Adelie Dream 39.6 18.1 186
95 Adelie Dream 36.2 17.3 187
96 Adelie Dream 40.8 18.9 208
97 Adelie Dream 38.1 18.6 190
98 Adelie Dream 40.3 18.5 196
99 Adelie Dream 33.1 16.1 178
100 Adelie Dream 43.2 18.5 192
101 Adelie Biscoe 35.0 17.9 192
102 Adelie Biscoe 41.0 20.0 203
103 Adelie Biscoe 37.7 16.0 183
104 Adelie Biscoe 37.8 20.0 190
105 Adelie Biscoe 37.9 18.6 193
106 Adelie Biscoe 39.7 18.9 184
107 Adelie Biscoe 38.6 17.2 199
108 Adelie Biscoe 38.2 20.0 190
109 Adelie Biscoe 38.1 17.0 181
110 Adelie Biscoe 43.2 19.0 197
111 Adelie Biscoe 38.1 16.5 198
112 Adelie Biscoe 45.6 20.3 191
113 Adelie Biscoe 39.7 17.7 193
114 Adelie Biscoe 42.2 19.5 197
115 Adelie Biscoe 39.6 20.7 191
116 Adelie Biscoe 42.7 18.3 196
117 Adelie Torgersen 38.6 17.0 188
118 Adelie Torgersen 37.3 20.5 199
119 Adelie Torgersen 35.7 17.0 189
120 Adelie Torgersen 41.1 18.6 189
121 Adelie Torgersen 36.2 17.2 187
122 Adelie Torgersen 37.7 19.8 198
123 Adelie Torgersen 40.2 17.0 176
124 Adelie Torgersen 41.4 18.5 202
125 Adelie Torgersen 35.2 15.9 186
126 Adelie Torgersen 40.6 19.0 199
127 Adelie Torgersen 38.8 17.6 191
128 Adelie Torgersen 41.5 18.3 195
129 Adelie Torgersen 39.0 17.1 191
130 Adelie Torgersen 44.1 18.0 210
131 Adelie Torgersen 38.5 17.9 190
132 Adelie Torgersen 43.1 19.2 197
133 Adelie Dream 36.8 18.5 193
134 Adelie Dream 37.5 18.5 199
135 Adelie Dream 38.1 17.6 187
136 Adelie Dream 41.1 17.5 190
137 Adelie Dream 35.6 17.5 191
138 Adelie Dream 40.2 20.1 200
139 Adelie Dream 37.0 16.5 185
140 Adelie Dream 39.7 17.9 193
141 Adelie Dream 40.2 17.1 193
142 Adelie Dream 40.6 17.2 187
143 Adelie Dream 32.1 15.5 188
144 Adelie Dream 40.7 17.0 190
145 Adelie Dream 37.3 16.8 192
146 Adelie Dream 39.0 18.7 185
147 Adelie Dream 39.2 18.6 190
148 Adelie Dream 36.6 18.4 184
149 Adelie Dream 36.0 17.8 195
150 Adelie Dream 37.8 18.1 193
151 Adelie Dream 36.0 17.1 187
152 Adelie Dream 41.5 18.5 201
153 Gentoo Biscoe 46.1 13.2 211
154 Gentoo Biscoe 50.0 16.3 230
155 Gentoo Biscoe 48.7 14.1 210
156 Gentoo Biscoe 50.0 15.2 218
157 Gentoo Biscoe 47.6 14.5 215
158 Gentoo Biscoe 46.5 13.5 210
159 Gentoo Biscoe 45.4 14.6 211
160 Gentoo Biscoe 46.7 15.3 219
161 Gentoo Biscoe 43.3 13.4 209
162 Gentoo Biscoe 46.8 15.4 215
163 Gentoo Biscoe 40.9 13.7 214
164 Gentoo Biscoe 49.0 16.1 216
165 Gentoo Biscoe 45.5 13.7 214
166 Gentoo Biscoe 48.4 14.6 213
167 Gentoo Biscoe 45.8 14.6 210
168 Gentoo Biscoe 49.3 15.7 217
169 Gentoo Biscoe 42.0 13.5 210
170 Gentoo Biscoe 49.2 15.2 221
171 Gentoo Biscoe 46.2 14.5 209
172 Gentoo Biscoe 48.7 15.1 222
173 Gentoo Biscoe 50.2 14.3 218
174 Gentoo Biscoe 45.1 14.5 215
175 Gentoo Biscoe 46.5 14.5 213
176 Gentoo Biscoe 46.3 15.8 215
177 Gentoo Biscoe 42.9 13.1 215
178 Gentoo Biscoe 46.1 15.1 215
179 Gentoo Biscoe 44.5 14.3 216
180 Gentoo Biscoe 47.8 15.0 215
181 Gentoo Biscoe 48.2 14.3 210
182 Gentoo Biscoe 50.0 15.3 220
183 Gentoo Biscoe 47.3 15.3 222
184 Gentoo Biscoe 42.8 14.2 209
185 Gentoo Biscoe 45.1 14.5 207
186 Gentoo Biscoe 59.6 17.0 230
187 Gentoo Biscoe 49.1 14.8 220
188 Gentoo Biscoe 48.4 16.3 220
189 Gentoo Biscoe 42.6 13.7 213
190 Gentoo Biscoe 44.4 17.3 219
191 Gentoo Biscoe 44.0 13.6 208
192 Gentoo Biscoe 48.7 15.7 208
193 Gentoo Biscoe 42.7 13.7 208
194 Gentoo Biscoe 49.6 16.0 225
195 Gentoo Biscoe 45.3 13.7 210
196 Gentoo Biscoe 49.6 15.0 216
197 Gentoo Biscoe 50.5 15.9 222
198 Gentoo Biscoe 43.6 13.9 217
199 Gentoo Biscoe 45.5 13.9 210
200 Gentoo Biscoe 50.5 15.9 225
201 Gentoo Biscoe 44.9 13.3 213
202 Gentoo Biscoe 45.2 15.8 215
203 Gentoo Biscoe 46.6 14.2 210
204 Gentoo Biscoe 48.5 14.1 220
205 Gentoo Biscoe 45.1 14.4 210
206 Gentoo Biscoe 50.1 15.0 225
207 Gentoo Biscoe 46.5 14.4 217
208 Gentoo Biscoe 45.0 15.4 220
209 Gentoo Biscoe 43.8 13.9 208
210 Gentoo Biscoe 45.5 15.0 220
211 Gentoo Biscoe 43.2 14.5 208
212 Gentoo Biscoe 50.4 15.3 224
213 Gentoo Biscoe 45.3 13.8 208
214 Gentoo Biscoe 46.2 14.9 221
215 Gentoo Biscoe 45.7 13.9 214
216 Gentoo Biscoe 54.3 15.7 231
217 Gentoo Biscoe 45.8 14.2 219
218 Gentoo Biscoe 49.8 16.8 230
219 Gentoo Biscoe 46.2 14.4 214
220 Gentoo Biscoe 49.5 16.2 229
221 Gentoo Biscoe 43.5 14.2 220
222 Gentoo Biscoe 50.7 15.0 223
223 Gentoo Biscoe 47.7 15.0 216
224 Gentoo Biscoe 46.4 15.6 221
225 Gentoo Biscoe 48.2 15.6 221
226 Gentoo Biscoe 46.5 14.8 217
227 Gentoo Biscoe 46.4 15.0 216
228 Gentoo Biscoe 48.6 16.0 230
229 Gentoo Biscoe 47.5 14.2 209
230 Gentoo Biscoe 51.1 16.3 220
231 Gentoo Biscoe 45.2 13.8 215
232 Gentoo Biscoe 45.2 16.4 223
233 Gentoo Biscoe 49.1 14.5 212
234 Gentoo Biscoe 52.5 15.6 221
235 Gentoo Biscoe 47.4 14.6 212
236 Gentoo Biscoe 50.0 15.9 224
237 Gentoo Biscoe 44.9 13.8 212
238 Gentoo Biscoe 50.8 17.3 228
239 Gentoo Biscoe 43.4 14.4 218
240 Gentoo Biscoe 51.3 14.2 218
241 Gentoo Biscoe 47.5 14.0 212
242 Gentoo Biscoe 52.1 17.0 230
243 Gentoo Biscoe 47.5 15.0 218
244 Gentoo Biscoe 52.2 17.1 228
245 Gentoo Biscoe 45.5 14.5 212
246 Gentoo Biscoe 49.5 16.1 224
247 Gentoo Biscoe 44.5 14.7 214
248 Gentoo Biscoe 50.8 15.7 226
249 Gentoo Biscoe 49.4 15.8 216
250 Gentoo Biscoe 46.9 14.6 222
251 Gentoo Biscoe 48.4 14.4 203
252 Gentoo Biscoe 51.1 16.5 225
253 Gentoo Biscoe 48.5 15.0 219
254 Gentoo Biscoe 55.9 17.0 228
255 Gentoo Biscoe 47.2 15.5 215
256 Gentoo Biscoe 49.1 15.0 228
257 Gentoo Biscoe 47.3 13.8 216
258 Gentoo Biscoe 46.8 16.1 215
259 Gentoo Biscoe 41.7 14.7 210
260 Gentoo Biscoe 53.4 15.8 219
261 Gentoo Biscoe 43.3 14.0 208
262 Gentoo Biscoe 48.1 15.1 209
263 Gentoo Biscoe 50.5 15.2 216
264 Gentoo Biscoe 49.8 15.9 229
265 Gentoo Biscoe 43.5 15.2 213
266 Gentoo Biscoe 51.5 16.3 230
267 Gentoo Biscoe 46.2 14.1 217
268 Gentoo Biscoe 55.1 16.0 230
269 Gentoo Biscoe 44.5 15.7 217
270 Gentoo Biscoe 48.8 16.2 222
271 Gentoo Biscoe 47.2 13.7 214
272 Gentoo Biscoe NA NA NA
273 Gentoo Biscoe 46.8 14.3 215
274 Gentoo Biscoe 50.4 15.7 222
275 Gentoo Biscoe 45.2 14.8 212
276 Gentoo Biscoe 49.9 16.1 213
277 Chinstrap Dream 46.5 17.9 192
278 Chinstrap Dream 50.0 19.5 196
279 Chinstrap Dream 51.3 19.2 193
280 Chinstrap Dream 45.4 18.7 188
281 Chinstrap Dream 52.7 19.8 197
282 Chinstrap Dream 45.2 17.8 198
283 Chinstrap Dream 46.1 18.2 178
284 Chinstrap Dream 51.3 18.2 197
285 Chinstrap Dream 46.0 18.9 195
286 Chinstrap Dream 51.3 19.9 198
287 Chinstrap Dream 46.6 17.8 193
288 Chinstrap Dream 51.7 20.3 194
289 Chinstrap Dream 47.0 17.3 185
290 Chinstrap Dream 52.0 18.1 201
291 Chinstrap Dream 45.9 17.1 190
292 Chinstrap Dream 50.5 19.6 201
293 Chinstrap Dream 50.3 20.0 197
294 Chinstrap Dream 58.0 17.8 181
295 Chinstrap Dream 46.4 18.6 190
296 Chinstrap Dream 49.2 18.2 195
297 Chinstrap Dream 42.4 17.3 181
298 Chinstrap Dream 48.5 17.5 191
299 Chinstrap Dream 43.2 16.6 187
300 Chinstrap Dream 50.6 19.4 193
301 Chinstrap Dream 46.7 17.9 195
302 Chinstrap Dream 52.0 19.0 197
303 Chinstrap Dream 50.5 18.4 200
304 Chinstrap Dream 49.5 19.0 200
305 Chinstrap Dream 46.4 17.8 191
306 Chinstrap Dream 52.8 20.0 205
307 Chinstrap Dream 40.9 16.6 187
308 Chinstrap Dream 54.2 20.8 201
309 Chinstrap Dream 42.5 16.7 187
310 Chinstrap Dream 51.0 18.8 203
311 Chinstrap Dream 49.7 18.6 195
312 Chinstrap Dream 47.5 16.8 199
313 Chinstrap Dream 47.6 18.3 195
314 Chinstrap Dream 52.0 20.7 210
315 Chinstrap Dream 46.9 16.6 192
316 Chinstrap Dream 53.5 19.9 205
317 Chinstrap Dream 49.0 19.5 210
318 Chinstrap Dream 46.2 17.5 187
319 Chinstrap Dream 50.9 19.1 196
320 Chinstrap Dream 45.5 17.0 196
321 Chinstrap Dream 50.9 17.9 196
322 Chinstrap Dream 50.8 18.5 201
323 Chinstrap Dream 50.1 17.9 190
324 Chinstrap Dream 49.0 19.6 212
325 Chinstrap Dream 51.5 18.7 187
326 Chinstrap Dream 49.8 17.3 198
327 Chinstrap Dream 48.1 16.4 199
328 Chinstrap Dream 51.4 19.0 201
329 Chinstrap Dream 45.7 17.3 193
330 Chinstrap Dream 50.7 19.7 203
331 Chinstrap Dream 42.5 17.3 187
332 Chinstrap Dream 52.2 18.8 197
333 Chinstrap Dream 45.2 16.6 191
334 Chinstrap Dream 49.3 19.9 203
335 Chinstrap Dream 50.2 18.8 202
336 Chinstrap Dream 45.6 19.4 194
337 Chinstrap Dream 51.9 19.5 206
338 Chinstrap Dream 46.8 16.5 189
339 Chinstrap Dream 45.7 17.0 195
340 Chinstrap Dream 55.8 19.8 207
341 Chinstrap Dream 43.5 18.1 202
342 Chinstrap Dream 49.6 18.2 193
343 Chinstrap Dream 50.8 19.0 210
344 Chinstrap Dream 50.2 18.7 198
body_mass_g sex year
1 3750 male 2007
2 3800 female 2007
3 3250 female 2007
4 NA <NA> 2007
5 3450 female 2007
6 3650 male 2007
7 3625 female 2007
8 4675 male 2007
9 3475 <NA> 2007
10 4250 <NA> 2007
11 3300 <NA> 2007
12 3700 <NA> 2007
13 3200 female 2007
14 3800 male 2007
15 4400 male 2007
16 3700 female 2007
17 3450 female 2007
18 4500 male 2007
19 3325 female 2007
20 4200 male 2007
21 3400 female 2007
22 3600 male 2007
23 3800 female 2007
24 3950 male 2007
25 3800 male 2007
26 3800 female 2007
27 3550 male 2007
28 3200 female 2007
29 3150 female 2007
30 3950 male 2007
31 3250 female 2007
32 3900 male 2007
33 3300 female 2007
34 3900 male 2007
35 3325 female 2007
36 4150 male 2007
37 3950 male 2007
38 3550 female 2007
39 3300 female 2007
40 4650 male 2007
41 3150 female 2007
42 3900 male 2007
43 3100 female 2007
44 4400 male 2007
45 3000 female 2007
46 4600 male 2007
47 3425 male 2007
48 2975 <NA> 2007
49 3450 female 2007
50 4150 male 2007
51 3500 female 2008
52 4300 male 2008
53 3450 female 2008
54 4050 male 2008
55 2900 female 2008
56 3700 male 2008
57 3550 female 2008
58 3800 male 2008
59 2850 female 2008
60 3750 male 2008
61 3150 female 2008
62 4400 male 2008
63 3600 female 2008
64 4050 male 2008
65 2850 female 2008
66 3950 male 2008
67 3350 female 2008
68 4100 male 2008
69 3050 female 2008
70 4450 male 2008
71 3600 female 2008
72 3900 male 2008
73 3550 female 2008
74 4150 male 2008
75 3700 female 2008
76 4250 male 2008
77 3700 female 2008
78 3900 male 2008
79 3550 female 2008
80 4000 male 2008
81 3200 female 2008
82 4700 male 2008
83 3800 female 2008
84 4200 male 2008
85 3350 female 2008
86 3550 male 2008
87 3800 male 2008
88 3500 female 2008
89 3950 male 2008
90 3600 female 2008
91 3550 female 2008
92 4300 male 2008
93 3400 female 2008
94 4450 male 2008
95 3300 female 2008
96 4300 male 2008
97 3700 female 2008
98 4350 male 2008
99 2900 female 2008
100 4100 male 2008
101 3725 female 2009
102 4725 male 2009
103 3075 female 2009
104 4250 male 2009
105 2925 female 2009
106 3550 male 2009
107 3750 female 2009
108 3900 male 2009
109 3175 female 2009
110 4775 male 2009
111 3825 female 2009
112 4600 male 2009
113 3200 female 2009
114 4275 male 2009
115 3900 female 2009
116 4075 male 2009
117 2900 female 2009
118 3775 male 2009
119 3350 female 2009
120 3325 male 2009
121 3150 female 2009
122 3500 male 2009
123 3450 female 2009
124 3875 male 2009
125 3050 female 2009
126 4000 male 2009
127 3275 female 2009
128 4300 male 2009
129 3050 female 2009
130 4000 male 2009
131 3325 female 2009
132 3500 male 2009
133 3500 female 2009
134 4475 male 2009
135 3425 female 2009
136 3900 male 2009
137 3175 female 2009
138 3975 male 2009
139 3400 female 2009
140 4250 male 2009
141 3400 female 2009
142 3475 male 2009
143 3050 female 2009
144 3725 male 2009
145 3000 female 2009
146 3650 male 2009
147 4250 male 2009
148 3475 female 2009
149 3450 female 2009
150 3750 male 2009
151 3700 female 2009
152 4000 male 2009
153 4500 female 2007
154 5700 male 2007
155 4450 female 2007
156 5700 male 2007
157 5400 male 2007
158 4550 female 2007
159 4800 female 2007
160 5200 male 2007
161 4400 female 2007
162 5150 male 2007
163 4650 female 2007
164 5550 male 2007
165 4650 female 2007
166 5850 male 2007
167 4200 female 2007
168 5850 male 2007
169 4150 female 2007
170 6300 male 2007
171 4800 female 2007
172 5350 male 2007
173 5700 male 2007
174 5000 female 2007
175 4400 female 2007
176 5050 male 2007
177 5000 female 2007
178 5100 male 2007
179 4100 <NA> 2007
180 5650 male 2007
181 4600 female 2007
182 5550 male 2007
183 5250 male 2007
184 4700 female 2007
185 5050 female 2007
186 6050 male 2007
187 5150 female 2008
188 5400 male 2008
189 4950 female 2008
190 5250 male 2008
191 4350 female 2008
192 5350 male 2008
193 3950 female 2008
194 5700 male 2008
195 4300 female 2008
196 4750 male 2008
197 5550 male 2008
198 4900 female 2008
199 4200 female 2008
200 5400 male 2008
201 5100 female 2008
202 5300 male 2008
203 4850 female 2008
204 5300 male 2008
205 4400 female 2008
206 5000 male 2008
207 4900 female 2008
208 5050 male 2008
209 4300 female 2008
210 5000 male 2008
211 4450 female 2008
212 5550 male 2008
213 4200 female 2008
214 5300 male 2008
215 4400 female 2008
216 5650 male 2008
217 4700 female 2008
218 5700 male 2008
219 4650 <NA> 2008
220 5800 male 2008
221 4700 female 2008
222 5550 male 2008
223 4750 female 2008
224 5000 male 2008
225 5100 male 2008
226 5200 female 2008
227 4700 female 2008
228 5800 male 2008
229 4600 female 2008
230 6000 male 2008
231 4750 female 2008
232 5950 male 2008
233 4625 female 2009
234 5450 male 2009
235 4725 female 2009
236 5350 male 2009
237 4750 female 2009
238 5600 male 2009
239 4600 female 2009
240 5300 male 2009
241 4875 female 2009
242 5550 male 2009
243 4950 female 2009
244 5400 male 2009
245 4750 female 2009
246 5650 male 2009
247 4850 female 2009
248 5200 male 2009
249 4925 male 2009
250 4875 female 2009
251 4625 female 2009
252 5250 male 2009
253 4850 female 2009
254 5600 male 2009
255 4975 female 2009
256 5500 male 2009
257 4725 <NA> 2009
258 5500 male 2009
259 4700 female 2009
260 5500 male 2009
261 4575 female 2009
262 5500 male 2009
263 5000 female 2009
264 5950 male 2009
265 4650 female 2009
266 5500 male 2009
267 4375 female 2009
268 5850 male 2009
269 4875 <NA> 2009
270 6000 male 2009
271 4925 female 2009
272 NA <NA> 2009
273 4850 female 2009
274 5750 male 2009
275 5200 female 2009
276 5400 male 2009
277 3500 female 2007
278 3900 male 2007
279 3650 male 2007
280 3525 female 2007
281 3725 male 2007
282 3950 female 2007
283 3250 female 2007
284 3750 male 2007
285 4150 female 2007
286 3700 male 2007
287 3800 female 2007
288 3775 male 2007
289 3700 female 2007
290 4050 male 2007
291 3575 female 2007
292 4050 male 2007
293 3300 male 2007
294 3700 female 2007
295 3450 female 2007
296 4400 male 2007
297 3600 female 2007
298 3400 male 2007
299 2900 female 2007
300 3800 male 2007
301 3300 female 2007
302 4150 male 2007
303 3400 female 2008
304 3800 male 2008
305 3700 female 2008
306 4550 male 2008
307 3200 female 2008
308 4300 male 2008
309 3350 female 2008
310 4100 male 2008
311 3600 male 2008
312 3900 female 2008
313 3850 female 2008
314 4800 male 2008
315 2700 female 2008
316 4500 male 2008
317 3950 male 2008
318 3650 female 2008
319 3550 male 2008
320 3500 female 2008
321 3675 female 2009
322 4450 male 2009
323 3400 female 2009
324 4300 male 2009
325 3250 male 2009
326 3675 female 2009
327 3325 female 2009
328 3950 male 2009
329 3600 female 2009
330 4050 male 2009
331 3350 female 2009
332 3450 male 2009
333 3250 female 2009
334 4050 male 2009
335 3800 male 2009
336 3525 female 2009
337 3950 male 2009
338 3650 female 2009
339 3650 female 2009
340 4000 male 2009
341 3400 female 2009
342 3775 male 2009
343 4100 male 2009
344 3775 female 2009Notice, that “penguin_data_from_base_r” now appears in the Environment tab of the upper right pane of the RStudio window. We can use this tab to quickly check the data we’ve loaded into R.
While the read.csv() function gets the job done, it doesn’t do a lot to format the data.
2.2 The readr package
As the name implies, the readr package contains functions designed to help us read tabular data from text files. These functions a lot of useful features to mark and handle problems we’re likely to encounter in real-world data (we’ll see some examples of this in later lessons).
To read this csv file, we’re going to use the read_csv() function from the readr package. The command is almost identical to base R, except we have an underscore in read_csv(), instead of a period.
penguin_data_from_readr <- read_csv(file = here::here("DATA/penguins.csv"))Rows: 344 Columns: 8
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (3): species, island, sex
dbl (5): bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g, year
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.The readr function includes some additional output that tells us about guesses it’s making about data in the file. Specifically, it determined the species, island, and sex columns contain text (or “characters”), while the remaining columns contain numbers with decimal points (or “doubles”).
Compare the the contents of the penguin_data_from_readr to the penguin_data_from_base_r data we loaded above:
penguin_data_from_readr# A tibble: 344 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Adelie Torgersen 39.1 18.7 181 3750
2 Adelie Torgersen 39.5 17.4 186 3800
3 Adelie Torgersen 40.3 18 195 3250
4 Adelie Torgersen NA NA NA NA
5 Adelie Torgersen 36.7 19.3 193 3450
6 Adelie Torgersen 39.3 20.6 190 3650
7 Adelie Torgersen 38.9 17.8 181 3625
8 Adelie Torgersen 39.2 19.6 195 4675
9 Adelie Torgersen 34.1 18.1 193 3475
10 Adelie Torgersen 42 20.2 190 4250
# ℹ 334 more rows
# ℹ 2 more variables: sex <chr>, year <dbl>readr functions read data from files and store them as tibbles, a special version of a data frame. We saw an example of a tibble when we worked with the penguins data frame, loaded by the palmerpenguins package.
3 Transforming penguins
3.1 The dplyr Package
The dplyr package comes with many functions for manipulating and extracting information from tabular data. As we’ll see below, dplyr functions are named after verbs that describe what we’re doing to the input data, and the first argument of every function is the input data frame (or tibble).
For simplicity, we’ll return to using the penguins data frame for the remainder of this lesson. While we could use dplyr functions to work with the data we read from penguins.csv, the penguins data frame has some nicer formatting.
3.2 Filter
We can use the filter() function to grab rows from our data that contain specific information. Here, we extract just those rows containing measurements from Gentoo penguins.
filter(penguins, species == "Gentoo")# A tibble: 124 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Gentoo Biscoe 46.1 13.2 211 4500
2 Gentoo Biscoe 50 16.3 230 5700
3 Gentoo Biscoe 48.7 14.1 210 4450
4 Gentoo Biscoe 50 15.2 218 5700
5 Gentoo Biscoe 47.6 14.5 215 5400
6 Gentoo Biscoe 46.5 13.5 210 4550
7 Gentoo Biscoe 45.4 14.6 211 4800
8 Gentoo Biscoe 46.7 15.3 219 5200
9 Gentoo Biscoe 43.3 13.4 209 4400
10 Gentoo Biscoe 46.8 15.4 215 5150
# ℹ 114 more rows
# ℹ 2 more variables: sex <fct>, year <int>Remember, the species information in our data frame is contained in the “species” column. This example code is telling R to search through every row in the penguin data, and return those rows that have “Gentoo” in the species column. From this example, we can see the general form for using the filter function: filter(dataset, comparison). We used “==” to indicate we want to find all rows in species that match the word “Gentoo”. This is an example of a relational operator.
Compare Values With Relational Operators
R supports several operators that let us compare values:
- == : Check if two values are exactly equal. Many programming languages use the double equals sign to indicate comparisons, because they’re already using the single equal sign for something else (e.g. assignment).
- <, > : Less-than, and greater-than comparisons.
- <=, >= : Less-than or equal, and greater-than or equal comparisons.
- != : Check if two values are not equal.
These operators return a logical value: TRUE or FALSE.
"Gentoo" == "Gentoo"[1] TRUE121 < 43[1] FALSEWe can also combine multiple filtering conditions in the same command. In this example, we want to get the rows containing data from female Adelie penguins.
filter(penguins,
sex == "female",
species == "Adelie")# A tibble: 73 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Adelie Torgersen 39.5 17.4 186 3800
2 Adelie Torgersen 40.3 18 195 3250
3 Adelie Torgersen 36.7 19.3 193 3450
4 Adelie Torgersen 38.9 17.8 181 3625
5 Adelie Torgersen 41.1 17.6 182 3200
6 Adelie Torgersen 36.6 17.8 185 3700
7 Adelie Torgersen 38.7 19 195 3450
8 Adelie Torgersen 34.4 18.4 184 3325
9 Adelie Biscoe 37.8 18.3 174 3400
10 Adelie Biscoe 35.9 19.2 189 3800
# ℹ 63 more rows
# ℹ 2 more variables: sex <fct>, year <int>Alternatively, we can combine multiple conditions with the & symbol (meaning “and”) and the | symbol (meaning “or”). We can re-write the previous filter command using the & symbol:
filter(penguins,
sex == "female" & species == "Adelie")# A tibble: 73 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Adelie Torgersen 39.5 17.4 186 3800
2 Adelie Torgersen 40.3 18 195 3250
3 Adelie Torgersen 36.7 19.3 193 3450
4 Adelie Torgersen 38.9 17.8 181 3625
5 Adelie Torgersen 41.1 17.6 182 3200
6 Adelie Torgersen 36.6 17.8 185 3700
7 Adelie Torgersen 38.7 19 195 3450
8 Adelie Torgersen 34.4 18.4 184 3325
9 Adelie Biscoe 37.8 18.3 174 3400
10 Adelie Biscoe 35.9 19.2 189 3800
# ℹ 63 more rows
# ℹ 2 more variables: sex <fct>, year <int>We can use the | symbol to retrieve data from female penguins that are either Adelie or Chinstrap:
filter(penguins,
sex == "female",
species == "Adelie" | species == "Chinstrap")# A tibble: 107 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Adelie Torgersen 39.5 17.4 186 3800
2 Adelie Torgersen 40.3 18 195 3250
3 Adelie Torgersen 36.7 19.3 193 3450
4 Adelie Torgersen 38.9 17.8 181 3625
5 Adelie Torgersen 41.1 17.6 182 3200
6 Adelie Torgersen 36.6 17.8 185 3700
7 Adelie Torgersen 38.7 19 195 3450
8 Adelie Torgersen 34.4 18.4 184 3325
9 Adelie Biscoe 37.8 18.3 174 3400
10 Adelie Biscoe 35.9 19.2 189 3800
# ℹ 97 more rows
# ℹ 2 more variables: sex <fct>, year <int>Let’s use the filter function to create a new data frame that just contains rows from our penguin data from Gentoo penguins.
gentoo_penguin_data <- filter(penguins, )3.3 Select
With the filter() function, we can choose which rows we want to extract from our data. If we want to choose which columns to extract, we use the select() function. Here we extract the columns containing the species, flipper length, and body mass from each penguin in the dataset.
select(penguins, species, flipper_length_mm, body_mass_g)# A tibble: 344 × 3
species flipper_length_mm body_mass_g
<fct> <int> <int>
1 Adelie 181 3750
2 Adelie 186 3800
3 Adelie 195 3250
4 Adelie NA NA
5 Adelie 193 3450
6 Adelie 190 3650
7 Adelie 181 3625
8 Adelie 195 4675
9 Adelie 193 3475
10 Adelie 190 4250
# ℹ 334 more rowsFrom this example code, we can see the general form of the select() function: select(dataset, column_name1, column_name2, ...). The select() function is very useful for reducing our dataset to just the columns we need for a particular calculation or analysis. This is critical when we’re working with input data that have 100s of columns.
Above, we created a data frame that only contains data from Gentoo penguins. Now let’s use the select() function on that data frame to extract the columns containing the species, flipper length, and body mass measurements. We’ll save the selected data frame in a new variable.
gentoo_body_and_flipper <- select(gentoo_penguin_data, species, )3.4 Mutate
If we want to add new columns to a data frame, We use the mutate() function. Here, we add a new column which contains the body mass of each penguins in kilograms (the “body_mass_g” column is in grams).
mutate(penguins,
body_mass_kg = body_mass_g / 1000)# A tibble: 344 × 9
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Adelie Torgersen 39.1 18.7 181 3750
2 Adelie Torgersen 39.5 17.4 186 3800
3 Adelie Torgersen 40.3 18 195 3250
4 Adelie Torgersen NA NA NA NA
5 Adelie Torgersen 36.7 19.3 193 3450
6 Adelie Torgersen 39.3 20.6 190 3650
7 Adelie Torgersen 38.9 17.8 181 3625
8 Adelie Torgersen 39.2 19.6 195 4675
9 Adelie Torgersen 34.1 18.1 193 3475
10 Adelie Torgersen 42 20.2 190 4250
# ℹ 334 more rows
# ℹ 3 more variables: sex <fct>, year <int>, body_mass_kg <dbl>From this code, we can see the general form of the mutate() function: mutate(dataset, new_column_name = expression). In this example, used the “/” operator to indicate we want to divide penguin body mass in grams by 1000, to calculate the body mass in kilograms. This is an example of an arithmetic operator.
Arithmetic Operators
R supports several operators that allow us to perform various mathematical operations:
- + addition
- - subtraction
- * multiplication
- / division
- ^ exponentiation
When we use these operators on the column of a data frame, they’re designed to work separately on each value in the column (called an “element wise” operation).
Note that mutate() adds new columns to the right side of the data frame. If we want to add new columns in different locations, we can use the .before and .after arguments.
mutate(penguins,
body_mass_kg = body_mass_g / 1000,
.after = body_mass_g)# A tibble: 344 × 9
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Adelie Torgersen 39.1 18.7 181 3750
2 Adelie Torgersen 39.5 17.4 186 3800
3 Adelie Torgersen 40.3 18 195 3250
4 Adelie Torgersen NA NA NA NA
5 Adelie Torgersen 36.7 19.3 193 3450
6 Adelie Torgersen 39.3 20.6 190 3650
7 Adelie Torgersen 38.9 17.8 181 3625
8 Adelie Torgersen 39.2 19.6 195 4675
9 Adelie Torgersen 34.1 18.1 193 3475
10 Adelie Torgersen 42 20.2 190 4250
# ℹ 334 more rows
# ℹ 3 more variables: body_mass_kg <dbl>, sex <fct>, year <int>We can provide .after and .before with either a column name (like we did above), or a number referring to the position in the table we want to insert the new column. Here we insert the new column before the current second column:
mutate(penguins,
body_mass_kg = body_mass_g / 1000,
.before = 2)# A tibble: 344 × 9
species body_mass_kg island bill_length_mm bill_depth_mm flipper_length_mm
<fct> <dbl> <fct> <dbl> <dbl> <int>
1 Adelie 3.75 Torgersen 39.1 18.7 181
2 Adelie 3.8 Torgersen 39.5 17.4 186
3 Adelie 3.25 Torgersen 40.3 18 195
4 Adelie NA Torgersen NA NA NA
5 Adelie 3.45 Torgersen 36.7 19.3 193
6 Adelie 3.65 Torgersen 39.3 20.6 190
7 Adelie 3.62 Torgersen 38.9 17.8 181
8 Adelie 4.68 Torgersen 39.2 19.6 195
9 Adelie 3.48 Torgersen 34.1 18.1 193
10 Adelie 4.25 Torgersen 42 20.2 190
# ℹ 334 more rows
# ℹ 3 more variables: body_mass_g <int>, sex <fct>, year <int>Let’s use the mutate() function to add a body mass (mg) column to the data frame of Gentoo data we’ve been working on so far. We’ll save this expanded data frame in a new variable.
gentoo_body_kg_and_flipper <- mutate(gentoo_body_and_flipper,)3.5 Pipes in R
R has a functionality allowing us to take the output of one function and provide it as input to another. The general name for this type of operation is “piping”. The pipe operator in R is |>. Here we use the filter() function, the R pipe (|>), and the head() function to view the first six rows returned by the filter function.
filter(penguins, species == "Gentoo") |> head()# A tibble: 6 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Gentoo Biscoe 46.1 13.2 211 4500
2 Gentoo Biscoe 50 16.3 230 5700
3 Gentoo Biscoe 48.7 14.1 210 4450
4 Gentoo Biscoe 50 15.2 218 5700
5 Gentoo Biscoe 47.6 14.5 215 5400
6 Gentoo Biscoe 46.5 13.5 210 4550
# ℹ 2 more variables: sex <fct>, year <int>In this R code, we use the filter() command to extract all of the rows from the data containing measurements from Gentoo penguins. We then use the |> operator to send the output of the filter() function to the head() function. The head() function returns the first 6 rows from its input data frame.
RStudio’s pipe shortcut
When working in RStudio, we can use the shortcut Ctrl/Cmd + Shift + M to enter the pipe.
Over the last three sections we used the filter(), select(), and mutate functions to create this data frame:
head(gentoo_body_kg_and_flipper)# A tibble: 6 × 1
species
<fct>
1 Adelie
2 Adelie
3 Adelie
4 Adelie
5 Adelie
6 Adelie We saved each of the intermediates to their own variables (take a look in the Environment tab to see the list of variables). Alternatively, we can use the pipe to generate the same data frame without saving any of the intermediate results:
penguins |>
filter(species == "Gentoo") |>
select(species, flipper_length_mm, body_mass_g) |>
mutate(body_mass_mg = body_mass_g * 1000,
.before = body_mass_g) |>
head()# A tibble: 6 × 4
species flipper_length_mm body_mass_mg body_mass_g
<fct> <int> <dbl> <int>
1 Gentoo 211 4500000 4500
2 Gentoo 230 5700000 5700
3 Gentoo 210 4450000 4450
4 Gentoo 218 5700000 5700
5 Gentoo 215 5400000 5400
6 Gentoo 210 4550000 4550With the pipe operator, we can combine many simple R functions to create complex pipelines, all while keeping our code readable.
Under the hood, the |> operator is taking the output of the function on its left and feeding it into the first argument of the function on its right. All dplyr functions are fully compatible with the pipe operator (the first argument of every function is the input data frame).
Another Pipe Operator:
%>%
The |> pipe operator is a relatively recent (May 2021) addition to base R. Before that, we needed to use the %>% operator, also called the “magrittr pipe.” This operator is still around and used in a lot of existing R code, but it requires us to load the magrittr package. In these lessons, we’ll only use the native |> pipe operator, so we don’t need to load any extra packages.
3.6 Arrange
We can use the arrange() function to sort the rows in our data according to the values in one or more columns. Here we sort the penguins by bill depth:
arrange(penguins, bill_depth_mm)# A tibble: 344 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Gentoo Biscoe 42.9 13.1 215 5000
2 Gentoo Biscoe 46.1 13.2 211 4500
3 Gentoo Biscoe 44.9 13.3 213 5100
4 Gentoo Biscoe 43.3 13.4 209 4400
5 Gentoo Biscoe 46.5 13.5 210 4550
6 Gentoo Biscoe 42 13.5 210 4150
7 Gentoo Biscoe 44 13.6 208 4350
8 Gentoo Biscoe 40.9 13.7 214 4650
9 Gentoo Biscoe 45.5 13.7 214 4650
10 Gentoo Biscoe 42.6 13.7 213 4950
# ℹ 334 more rows
# ℹ 2 more variables: sex <fct>, year <int>By default, arrange() sorts values from smallest to largest (ascending). We can use the desc() function inside arrange to sort values from largest to smallest (descending).
arrange(penguins, desc(bill_depth_mm))# A tibble: 344 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Adelie Torgers… 46 21.5 194 4200
2 Adelie Torgers… 38.6 21.2 191 3800
3 Adelie Dream 42.3 21.2 191 4150
4 Adelie Torgers… 34.6 21.1 198 4400
5 Adelie Dream 39.2 21.1 196 4150
6 Adelie Biscoe 41.3 21.1 195 4400
7 Chinstrap Dream 54.2 20.8 201 4300
8 Adelie Torgers… 42.5 20.7 197 4500
9 Adelie Biscoe 39.6 20.7 191 3900
10 Chinstrap Dream 52 20.7 210 4800
# ℹ 334 more rows
# ℹ 2 more variables: sex <fct>, year <int>Lastly, we can sort data based on multiple columns:
arrange(penguins, island, desc(bill_depth_mm))# A tibble: 344 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Adelie Biscoe 41.3 21.1 195 4400
2 Adelie Biscoe 39.6 20.7 191 3900
3 Adelie Biscoe 45.6 20.3 191 4600
4 Adelie Biscoe 41 20 203 4725
5 Adelie Biscoe 37.8 20 190 4250
6 Adelie Biscoe 38.2 20 190 3900
7 Adelie Biscoe 42 19.5 200 4050
8 Adelie Biscoe 42.2 19.5 197 4275
9 Adelie Biscoe 35.9 19.2 189 3800
10 Adelie Biscoe 37.6 19.1 194 3750
# ℹ 334 more rows
# ℹ 2 more variables: sex <fct>, year <int>3.7 Distinct
The distinct() function returns all unique rows from the data frame. We can provide the names of the columns we want to search for unique combinations. Here we want to find all unique combinations of species and island.
distinct(penguins, species, island)# A tibble: 5 × 2
species island
<fct> <fct>
1 Adelie Torgersen
2 Adelie Biscoe
3 Adelie Dream
4 Gentoo Biscoe
5 Chinstrap Dream If we don’t specify any column names, the distinct() function will look for unique combinations across all columns.
distinct(penguins)# A tibble: 344 × 8
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
<fct> <fct> <dbl> <dbl> <int> <int>
1 Adelie Torgersen 39.1 18.7 181 3750
2 Adelie Torgersen 39.5 17.4 186 3800
3 Adelie Torgersen 40.3 18 195 3250
4 Adelie Torgersen NA NA NA NA
5 Adelie Torgersen 36.7 19.3 193 3450
6 Adelie Torgersen 39.3 20.6 190 3650
7 Adelie Torgersen 38.9 17.8 181 3625
8 Adelie Torgersen 39.2 19.6 195 4675
9 Adelie Torgersen 34.1 18.1 193 3475
10 Adelie Torgersen 42 20.2 190 4250
# ℹ 334 more rows
# ℹ 2 more variables: sex <fct>, year <int>3.8 Grouping and Summarizing Data
We started transforming these data because we wanted to extract some summary stats about the body mass and flipper length of the three penguin species in our data. With the functions we’ve used so far, we can extract the data we need for a specific penguin species. Here, we use the summarize() function to calculate the mean mean body mass and flipper length across all Gentoo penguins in our data.
penguins |>
filter(species == "Gentoo") |>
select(species, flipper_length_mm, body_mass_g) |>
# na.omit() |> # Filter out any rows containing NA values in any columns
summarize(mean_body_mass_g = mean(body_mass_g),
mean_flipper_length_mm = mean(flipper_length_mm))# A tibble: 1 × 2
mean_body_mass_g mean_flipper_length_mm
<dbl> <dbl>
1 NA NAIn this example we use dplyr functions and the pipe operator to filter our data for Gentoo penguins, select our columns of interest (body_mass_g and flipper_length_mm), and the summarize() function (also from dplyr) to calculate the mean values across data in the body_mass_g and flipper_length_mm columns.
Missing Data
The first time we ran the code above, we didn’t use the na.omit() function and our mean body mass and flipper length calculations returned ‘NA’ values. ‘NA’ is one of the ways R represents missing data, and it turns out one of the penguins in our dataset has ‘NA’ values for all of its measurements (you can find it by looking through the data with the View() function). Many function that perform mathematical operations (like mean), will return an ‘NA’ value if any of its inputs are ‘NA’. This is so we’re aware there are ‘NA’ values present in our data and can handle them accordingly. Once we realized there was a single ‘NA’ values in our data, we excluded it using the na.omit() function.
From this code, we see the general form of the summarize() function: summarize(dataset, column_name = expression). This is quite similar to the mutate() function. However, while the mutate() function performs a calculation for each row in a data frame column, the summarize() function performs one calculation using all of the data in a column.
Using the summarize() function we can quickly calculate summary stats from the columns in a data frame. In order to get the same summary stats for the other penguin data, we’d need to repeat the same set of operations two more times (one for each species). Ideally, we want to be able to work on data from all three penguin species at the same time.
We can do this with the group_by() function.
penguins |>
select(species, body_mass_g, flipper_length_mm) |>
group_by(species) |>
# This time we're using arguements in the mean function to remove the NA values
summarize(mean_body_mass_g = mean(body_mass_g, na.rm = TRUE),
mean_flipper_length_mm = mean(flipper_length_mm, na.rm = TRUE))# A tibble: 3 × 3
species mean_body_mass_g mean_flipper_length_mm
<fct> <dbl> <dbl>
1 Adelie 3701. 190.
2 Chinstrap 3733. 196.
3 Gentoo 5076. 217.Here we use the group_by() function to group the data according to the species column, before using the summarize() function. This grouping causes the summarize() function to perform calculations across the data within each group (species, in this case), rather than across the entire data frame.
The means don’t give us the whole picture, so let’s calculate the standard deviations for each of these measurements, as well as the total number of penguins from each species.
penguins |>
select(species, body_mass_g, flipper_length_mm) |>
group_by(species) |>
na.omit() |>
summarize(mean_body_mass_g = mean(body_mass_g),
sd_body_mass_g = sd(body_mass_g),
mean_flipper_length_mm = mean(flipper_length_mm),
sd_mean_flipper_length_mm = sd(flipper_length_mm),
Total_animals = n())It looks like the raw numbers agree with what we saw in the figure we generated in the previous lesson. Namely, the Gentoo penguins tend to have more mass and longer flippers than the other two species. And while the Chinstrap penguins have higher mean body mass and flipper length than the Adélie penguins, the standard deviations in these measurements are large enough that there probably isn’t a significant difference in size between the two. In the coming lessons, we’ll apply some statistical tests to these data to test our hypotheses.
Not all operations are equivalent
So far, we’ve seen two ways of keeping ‘NA’ values from affecting our calculations: 1. The na.omit() function removes all rows from a data frame that contain ‘NA’ values in any column. 2. The mean() and sd() have an na.rm argument that excludes all ‘NA’ values from the mean / standard deviation calculations when we set it to TRUE (na.rm = TRUE).
We used the na.omit() function to exclude the ‘NA’ values before we used the summarize() function to calculated all of our summary statistics above. However, if we skip the na.omit() function and instead use the “na.rm” argument for mean() and sd() to exclude the ‘NA’ values, we get a slightly different result.
penguins |>
select(species, body_mass_g, flipper_length_mm) |>
group_by(species) |>
# na.omit() |>
summarize(mean_body_mass_g = mean(body_mass_g, na.rm = TRUE),
sd_body_mass_g = sd(body_mass_g, na.rm = TRUE),
mean_flipper_length_mm = mean(flipper_length_mm, na.rm = TRUE),
sd_mean_flipper_length_mm = sd(flipper_length_mm, na.rm = TRUE),
Total_animals = n())# A tibble: 3 × 6
species mean_body_mass_g sd_body_mass_g mean_flipper_length_mm
<fct> <dbl> <dbl> <dbl>
1 Adelie 3701. 459. 190.
2 Chinstrap 3733. 384. 196.
3 Gentoo 5076. 504. 217.
# ℹ 2 more variables: sd_mean_flipper_length_mm <dbl>, Total_animals <int>Compare these results to the previous code using na.omit(), paying close attention to the “Total_animals” column. If there are too many columns in the results to compare them easily, you could always use the select() function to grab just the “species” and “Total_animals” columns.
When we used the na.rm argument approach, we ended with with one extra penguin in the “Total_animals” column for the Adélie and Gentoo penguins. This is because the n() function counts rows, regardless of their contents (you can confirm there’s no na.rm argument for n() using the R docs). While there are many different ways to accomplish the same task, they are not all equivalent in all cases. If we weren’t also using the n() function to count the total number of penguins in each species, both of our methods for removing the ‘NA’ values would have produced the same result.
Even though this is a toy example, we’ve created a flexible analysis pipeline by combining these dplyr functions, The code we’ve written will still work if we collect new data from different penguin species, add additional biological measurements beyond body mass and flipper length, or remove some of the rows from the original input data.
4 R session information
Here we report the version number for R and the package versions we used to perform the analyses in this document.
sessionInfo()R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/New_York
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] dplyr_1.1.2 readr_2.1.4 palmerpenguins_0.1.1
[4] here_1.0.1
loaded via a namespace (and not attached):
[1] crayon_1.5.2 vctrs_0.6.3 cli_3.6.1 knitr_1.43
[5] rlang_1.1.1 xfun_0.39 generics_0.1.3 jsonlite_1.8.7
[9] bit_4.0.5 glue_1.6.2 rprojroot_2.0.3 htmltools_0.5.5
[13] hms_1.1.3 fansi_1.0.4 rmarkdown_2.23 evaluate_0.21
[17] tibble_3.2.1 tzdb_0.4.0 fastmap_1.1.1 yaml_2.3.7
[21] lifecycle_1.0.3 compiler_4.3.1 htmlwidgets_1.6.2 pkgconfig_2.0.3
[25] rstudioapi_0.15.0 digest_0.6.33 R6_2.5.1 tidyselect_1.2.0
[29] utf8_1.2.3 parallel_4.3.1 vroom_1.6.3 pillar_1.9.0
[33] magrittr_2.0.3 withr_2.5.0 bit64_4.0.5 tools_4.3.1